SOTAVerified

Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

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Showing 1851–1875 of 2041 papers

TitleStatusHype
Double Multi-Head Attention Multimodal System for Odyssey 2024 Speech Emotion Recognition Challenge—0
DSNet: Disentangled Siamese Network with Neutral Calibration for Speech Emotion Recognition—0
DuA: Dual Attentive Transformer in Long-Term Continuous EEG Emotion Analysis—0
Dual-GAN: Joint BVP and Noise Modeling for Remote Physiological Measurement—0
Dual Prototyping with Domain and Class Prototypes for Affective Brain-Computer Interface in Unseen Target Conditions—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection—0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets—0
Adaptive Fusion Techniques for Multimodal Data—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—0
Dynamic Layer Customization for Noise Robust Speech Emotion Recognition in Heterogeneous Condition Training—0
Dynamic Modality and View Selection for Multimodal Emotion Recognition with Missing Modalities—0
Dynamic Model of Facial Expression Recognition based on Eigen-face Approach—0
Dynamic Resolution Guidance for Facial Expression Recognition—0
Dynamic Time-Alignment of Dimensional Annotations of Emotion using Recurrent Neural Networks—0
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]—0
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model—0
Early Joint Learning of Emotion Information Makes MultiModal Model Understand You Better—0
ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition—0
Edge Based Grid Super-Imposition for Crowd Emotion Recognition—0
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis—0
EEG based Emotion Recognition: A Tutorial and Review—0
EEG based Emotion Recognition of Image Stimuli—0
EEG-based Emotion Recognition via Efficient Convolutional Neural Network and Contrastive Learning—0
EEG-based Emotion Style Transfer Network for Cross-dataset Emotion Recognition—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified